Mid-size hotels in Central Europe are increasingly adopting AI chatbots to enhance
customer service. They must,
however, balance innovation with strict GDPR requirements.This study compares a cloud-based
large language
model (OpenAI’s GPT-4 API) against an on-premises deployment of an open-source LLM
(Llama 3–13B) for
hotel customer-service chatbots. We evaluate both solutions in the context of Cogniforce
Labs deploying
chatbots for regional hotels, focusing on GDPR compliance, operational cost, response
latency, and customer
satisfaction. Using three common use cases (booking modification, late check-in, and
local recommendations),
we simulate chatbot interactions and measure performance. The results show that the
on-premises LLM offers
superior data privacy (all guest data remains in-house, aiding GDPR compliance) and
lower latency (up to ~35%
faster responses), along with a more predictable cost structure. The cloud GPT-4 solution,
however, delivers
slightly higher answer quality, yielding greater customer satisfaction scores, at
the expense of transmitting
personal data to a third-party and incurring usage-based fees. Our findings suggest
a trade-off between
compliance/cost and service quality. Hotels prioritizing privacy may favor on-premise
LLMs, while those
emphasizing customer experience might opt for cloud AI with proper safeguards. We
discuss hybrid strategies
and provide recommendations for hospitality businesses navigating this choice.
